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This paper considers generalizations of certain arithmetic complexes appearing in the work of Raicu and VandeBogert in connection with the study of stable sheaf cohomology on flag varieties. Defined over the ring of integer valued polynomials, we prove an isomorphism of these complexes as conjectured by Gao, Raicu, and VandeBogert. In particular, this shows that a previously made identification between the stable sheaf cohomology of hook and two column partition Schur functors applied to the cotangent sheaf of projective space can be made to be uniform with respect to these complexes. These results are extended to the projective space defined over the integers. 

Name-based publish/subscribe systems using Information-Centric Networking (ICN) principles can provide a flexible and efficient framework for communication in disaster situations. Efficient, secure dissemination of information can play a critical role in disaster management. But, secure and authenticated group communications that maintain confidentiality and integrity remain a challenge. In this paper, we design a flexible and efficient encryption framework SAFE that leverages graph-based naming frameworks for providing role-based communication among first responders. We study the suitability of message-oriented encryption where the sender leverages the name hierarchy, and compare it with a key-oriented encryption scheme that requires the receiver to utilize appropriate keys to decrypt based on the publisher-targeted name for the message. Both encryption schemas can be built with attribute-based encryption (ABE) or public key encryption (PKE) implementations. We find message-oriented encryption provides the needed flexibility for dynamic environments when communicating with members changes frequently. With message-oriented encryption, we further address key revocation and support for infrastructure-less environments in disaster situations and consider the tradeoff between flexibility and optimization for large relatively static communication groups. We evaluate both encryption schemas built on top of ABE and PKE. We examine the key generation time, ciphertext length, encryption, and decryption time, and see that SAFE's design is the most suitable for large and dynamically changing groups. 

Efficient and secure message dissemination plays an important role during a disaster environment. Name-based publish/subscribe systems, especially role-based names, using principles of Information-Centricity provide an efficient frame-work for communications among first responders. However, a challenge is maintaining confidentiality during communication. We have developed an encryption framework that leverages graph-based naming systems which provides role-based communication among first responders. Our framework is built on top of the dynamic role-based names and can be implemented using attribute-based encryption (ABE) or public key encryption (PKE). In this demo, we show the operations of our framework in a typical scenario of first responders using the application. 

Timely delivery of the right information to the right first responders can help improve the outcomes of their efforts and save lives. With social media communications (Twitter, Facebook, etc.) being increasingly used to send and get information during disasters, forwarding them to the right first responders in a timely manner can be very helpful. We use Natural Language Processing and Machine Learning, to steer the social media posts to the most appropriate first responder.An important goal is to retrieve and deliver only the critical, actionable information to first responders in real-time. We examine the overall pipeline starting from retrieving tweets from the social media platforms, to their classification, and dissemination to first responders.We propose improvements in the area of data retrieval, relevance prediction and prioritizing information sent to the first responders by fusing NLP and ML classification techniques thus improving emergency response. We demonstrate the effectiveness of our proposed approach in retrieving and extracting 37,295 actionable tweets related to the IDA hurricane that occurred in the US in Aug.–Sep, 2021.